{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "month=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ff4765cf60>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.15058385039641503, 0.14672742157876595, 0.11568209238193726, 0.06608712512926578, 0.520919510513616]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age of Jobs')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
